Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion (2311.01017v4)

Abstract: Learning world models can teach an agent how the world works in an unsupervised manner. Even though it can be viewed as a special case of sequence modeling, progress for scaling world models on robotic applications such as autonomous driving has been somewhat less rapid than scaling LLMs with Generative Pre-trained Transformers (GPT). We identify two reasons as major bottlenecks: dealing with complex and unstructured observation space, and having a scalable generative model. Consequently, we propose Copilot4D, a novel world modeling approach that first tokenizes sensor observations with VQVAE, then predicts the future via discrete diffusion. To efficiently decode and denoise tokens in parallel, we recast Masked Generative Image Transformer as discrete diffusion and enhance it with a few simple changes, resulting in notable improvement. When applied to learning world models on point cloud observations, Copilot4D reduces prior SOTA Chamfer distance by more than 65% for 1s prediction, and more than 50% for 3s prediction, across NuScenes, KITTI Odometry, and Argoverse2 datasets. Our results demonstrate that discrete diffusion on tokenized agent experience can unlock the power of GPT-like unsupervised learning for robotics.

• The paper introduces Copilot4D, which learns unsupervised world models for autonomous driving by tokenizing sensor data and applying discrete diffusion for predicting future observations.
• Evaluated on standard datasets, Copilot4D significantly reduces Chamfer distance for point cloud forecasting, achieving over 65% improvement for 1-second predictions.
• This approach offers enhanced prediction accuracy crucial for real-time decision-making and demonstrates scalability potential for unsupervised world models in broader robotics applications.

Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion

The pursuit of effective models for autonomous driving has led to increasing interest in world modeling approaches, particularly those leveraging unsupervised methods. The paper "Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion" presents a novel approach addressing the challenge of developing scalable and effective world models for autonomous driving. The authors propose Copilot4D, a method that employs the discrete diffusion mechanism to predict future sensor observations, which promises substantial improvements over prior state-of-the-art methods in point cloud forecasting tasks.

Challenges and Innovations

Two primary bottlenecks hinder the scaling of world models in autonomous applications: managing complex observation spaces and developing scalable generative models. Copilot4D addresses both issues innovatively:

1. Tokenization of Observation Space: The method begins by tokenizing sensor observations using Vector Quantized Variational AutoEncoder (VQVAE). This approach simplifies the observation space by converting continuous data into discrete tokens, similar to handling sequences in NLP using Generative Pre-trained Transformers (GPT).
2. Discrete Diffusion for Prediction: To predict future observations, Copilot4D employs discrete diffusion on tokenized agent experiences, adapting methods from image generation frameworks like Masked Generative Image Transformer (MaskGIT). This recasting into a discrete diffusion framework allows efficient parallel decoding and denoising of tokens.

Performance Evaluation

When assessed using standard autonomous driving datasets such as NuScenes, KITTI Odometry, and Argoverse2, Copilot4D demonstrates significant improvements:

• It reduces the Chamfer distance, a pivotal metric in point cloud forecasting, by over 65% for 1 second predictions and more than 50% for 3 seconds predictions compared to previous methods. The results indicate that Copilot4D's approach to discrete diffusion effectively models the future state of environments from past observations and actions.

Implications and Future Work

The introduction of Copilot4D has several practical implications for autonomous driving:

• Enhanced Prediction Accuracy: The reduced Chamfer distance suggests better accuracy in predicting spatial configurations, crucial for applications requiring real-time processing and decision-making.
• Scalability in Robotics: By leveraging discrete diffusion and the tokenization approach akin to LLMs, the method demonstrates a promising pathway for scaling unsupervised world models in other robotics applications.

Theoretical implications include advancing the understanding of sequence modeling in robotics and encouraging future research in discrete diffusion methods, potentially integrating them with reinforcement learning frameworks to refine decision-making processes.

The paper successfully illustrates how discrete diffusion on tokenized observations unlocks GPT-like unsupervised learning potential in robotics. The emerging possibility of integrating model-based reinforcement learning with Copilot4D's world modeling paradigm holds promise for refining autonomous systems' decision-making capabilities. Future work may explore integrating larger datasets, refining tokenization methods, or enhancing model architectures to facilitate complex agent-environment interactions within diverse autonomous applications.